Data profiling is the essential discovery process that helps you analyze, classify, cleanse, integrate, mask, and report on data in your repositories. With the information profile processes produce, you can make decisions about data integration and analysis, migration and masking, etc.

Update: Q2’16: In addition to the database profiling wizard in the data discovery menu group in IRI Workbench described below, IRI has introduced robust data classification that enables the application of field rules for multi-source data transformation and protection through data class libraries.

Data and database migration are key considerations for any system implementation, upgrade, or consolidation. Data migration happens for many reasons, like appliance upgrades or enhancements, server maintenance, or data center relocation.

Lines of business do not think in terms of metadata per se. Users want to know what information is available, and where it is. They want to know if the data is reliable, protected, who’s using it, where it came from and how it’s been changed.

Data profiling, or data discovery, refers to the process of obtaining information from, and descriptive statistics about, various sources of data. The purpose of data profiling is to get a better understanding of the content of data, as well as its structure, relationships, and current levels of accuracy and integrity.